Tatsuya Daikoku
Papers
1
Total Citations
11
H-Index
1
About
Tatsuya Daikoku is a leading researcher at the intersection of computational neuroscience, artificial intelligence, and robotics, with a primary focus on homeostatic reinforcement learning (RL) and autonomous behavior. His most-cited work, "Emergence of integrated behaviors through direct optimization for homeostasis" (2024, 11 citations), introduces a groundbreaking framework where artificial agents learn to organize complex, integrated behaviors by directly optimizing for internal physiological stability—mimicking the self-regulatory processes seen in living organisms. This approach challenges traditional reward-based RL models by grounding behavior in the fundamental biological principle of homeostasis. Daikoku’s contributions are pivotal for advancing embodied AI and understanding how autonomous systems can develop adaptive, survival-driven actions without explicit external rewards. His research has garnered attention for bridging theoretical neuroscience with practical robotics, offering a novel pathway toward more resilient and lifelike artificial agents. By demonstrating that machines can autonomously learn to maintain internal states through dynamic behavioral strategies, Daikoku is shaping the future of intelligent systems that operate robustly in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1